Is this the end of the road for variance analysis?

Variance analysis has been a key tool in the arsenal of management accountants for the last century. But is it really as valuable as we think?

Steve Morlidge, experienced management accountant and business author, is not a fan of variance analysis. It isn’t valuable in the modern workplace, he says.

We have so much data available to us now that accountants could be offering a lot more in their analysis, he explains. Variance analysis tables don’t give much value to the managers it’s aimed at.

 “Just take one row, not all the tables that adorn your 660-page management reports that you impose on people every month. What’s the narrative, what is this telling us? Is this good or bad, are things getting better or worse? Let’s say the year-to-date revenue is below the budget, but it’s better than last year. So does that make it good or bad? Which is the best comparator?”

Morlidge believes that there are three main problems with variance analysis:

1. The information isn’t palatable

Finance professionals take what they’re doing for granted and don’t take the time to make the information palatable for others in the organisation. As a result, it’s rarely used to drive any real action, and persistent problems go unidentified.

2. The data points are limited

A limited number of data points are used to create the analysis, which doesn’t take into account additional information that might give it context. Without context, it’s hard to identify what the organisation should do to make improvements

3.It’s hard to read

Variance analysis tables run counterintuitively to the way our brains interpret data. “From an evolutionary perspective, our brains aren’t designed to deal with this stuff.”

So what’s the alternative?

Morlidge believes that accountants should do away with variance analysis altogether. More accountants are using data visualisations to help non-accountants interpret the data, but it doesn’t solve the problem of “noise” in the data, or take into account any context. It’s very easy to see patterns in the data that aren’t really there, says Morlidge: “A bit like when you look at the clouds in the sky and you see a face – that’s what happens to our brains when we see noise, we’re looking for patterns that might not be there.”

Moving annual totals

Morlidge recommends using a graph called a moving annual total, which is the total value of a variable – for example, sales – over the course of the previous 12 months. It’s a rolling annual sum, changing at the end of each month as new data is added to it. So simply put, the moving annual total for January 2020 would be January 2019 + February 2019 + March 2019 and so on. 

“If you do an average of anything, which is what it kind of is, you filter out noise,” says Morlidge. “Secondly, most of the seasonality we see in data has an annual cycle. So if you’re using a moving annual total, you’re filtering out the seasonality as well.” 

Morlidge argues that this makes it easy for everyone to see what’s going on. “If you understand what you’re doing, and you know how to construct graphs in a way that our brains can assimilate easily, it becomes really obvious what’s going on.”

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